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        <title>The repetition rate of text as a predictor of the effectiveness of machine translation adaptation</title>
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    <name type="personal">
        <namePart type="given">Mauro</namePart>
        <namePart type="family">Cettolo</namePart>
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    <name type="personal">
        <namePart type="given">Nicola</namePart>
        <namePart type="family">Bertoldi</namePart>
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    <name type="personal">
        <namePart type="given">Marcello</namePart>
        <namePart type="family">Federico</namePart>
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        <dateIssued>2014-oct 22-26</dateIssued>
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            <title>Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track</title>
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        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Michel</namePart>
            <namePart type="family">Simard</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Since the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of looking for and evaluating text features that might help the prediction of the impact of MT adaptation on translation quality. In particular, the repetition rate metric, we recently proposed, is compared to other features employed in very related NLP tasks. The comparison is carried out through a regression analysis between feature values and MT performance gains by dynamically adapted versus non-adapted MT engines, on five different translation tasks. The main outcome of experiments is that the repetition rate correlates better than any other considered feature with the MT gains yielded by the online adaptation, although using all features jointly results in better predictions than with any single feature.</abstract>
    <identifier type="citekey">cettolo-etal-2014-repetition</identifier>
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        <url>https://aclanthology.org/2014.amta-researchers.13</url>
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        <date>2014-oct 22-26</date>
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            <start>166</start>
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%0 Conference Proceedings
%T The repetition rate of text as a predictor of the effectiveness of machine translation adaptation
%A Cettolo, Mauro
%A Bertoldi, Nicola
%A Federico, Marcello
%Y Al-Onaizan, Yaser
%Y Simard, Michel
%S Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
%D 2014
%8 oct 22 26
%I Association for Machine Translation in the Americas
%C Vancouver, Canada
%F cettolo-etal-2014-repetition
%X Since the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of looking for and evaluating text features that might help the prediction of the impact of MT adaptation on translation quality. In particular, the repetition rate metric, we recently proposed, is compared to other features employed in very related NLP tasks. The comparison is carried out through a regression analysis between feature values and MT performance gains by dynamically adapted versus non-adapted MT engines, on five different translation tasks. The main outcome of experiments is that the repetition rate correlates better than any other considered feature with the MT gains yielded by the online adaptation, although using all features jointly results in better predictions than with any single feature.
%U https://aclanthology.org/2014.amta-researchers.13
%P 166-179

Markdown (Informal)

[The repetition rate of text as a predictor of the effectiveness of machine translation adaptation](https://aclanthology.org/2014.amta-researchers.13) (Cettolo et al., AMTA 2014)

• The repetition rate of text as a predictor of the effectiveness of machine translation adaptation (Cettolo et al., AMTA 2014)

ACL

• Mauro Cettolo, Nicola Bertoldi, and Marcello Federico. 2014. The repetition rate of text as a predictor of the effectiveness of machine translation adaptation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 166–179, Vancouver, Canada. Association for Machine Translation in the Americas.